The course is part of these learning paths
Google Data Studio is a web-based application for creating reports and dashboards. It’s an easy-to-use tool for displaying your data visually. It was designed to help Google Analytics users create custom reports, but it can now read data from many sources, including BigQuery, Cloud SQL, and Cloud Storage.
In this course, you will learn how to connect a Data Studio report to a BigQuery dataset, visualize it with charts and graphs, and share it with your co-workers to make data-driven decisions.
- Create a report in Data Studio
- Connect a Data Studio report to a BigQuery dataset
- Share a Data Studio report with appropriate levels of access
- Data professionals, especially those who work with big data
- People studying for the Google Professional Data Engineer exam
- “Introduction to Google BigQuery” course or experience with BigQuery
- A Google Cloud Platform account (sign up for a free trial at https://cloud.google.com/free if you don’t have an account)
If you look at the list of Google Cloud Platform products, you’ll see Google Data Studio in the Big Data section, but if you look in the Cloud Platform menu, it’s not there. That’s because it isn’t a GCP service. It was designed as a reporting application for Google Analytics, so it’s more like Google Docs or Google Sheets than a GCP service like BigQuery.
That’s actually quite fortunate because Google has made it as easy to use as possible for its Google Analytics customers. Later on in the course, I’ll show you the stark contrast in design approaches between Data Studio and a GCP visualization service called Cloud Datalab.
By the way, you might see references to Data Studio 360 in some places. That’s because there used to be two different versions, but now there’s just one product called Data Studio.
Let’s start by creating a simple Google Analytics report so you can see how Data Studio works before we use it with BigQuery data. Go to datastudio.google.com. On your first time, it’ll bring up a few extra dialog boxes, including a click-through agreement. I don’t see those because I’ve been here before.
Once you’re in, you can start from one of these templates or you can start from scratch. Click the blank one.
At a high-level, there are 3 steps to take: connect, visualize, and share. Connect means to select a data source. Then you visualize the data by creating charts and graphs. And finally, you can share the report with other people.
Google has provided some sample data sources, so we’ll choose one of them to get started. Select the sample Google Analytics data. Now you have to confirm that you want to add the data source to your report. This might seem unnecessary. After all, you just selected the data source to add, so why wouldn’t you want to add it? Well, this dialog box is reminding us that when you share a report and allow other people to edit it, they can access other parts of this data source that you didn’t include in your report.
We’re not worried about that with this sample data, so click the “Add to Report” button. You also have to allow access to Google Drive because that’s where it stores the reports.
OK, this is a pretty boring report, isn’t it? When you add a data source, it doesn’t do anything with it until you tell it what you want. This is the visualization step.
First, you have to choose one of the chart types or other visual elements up here. When I’m not completely familiar with a data source, I like to start by selecting the Table option, which will show you the raw data. Then draw a box on the page.
It shows session data for a website. The “Medium” column says where the traffic came from, such as organic, which means from a search engine; referral, which means a person clicked on a link to this site; and cpc, which stands for “cost per click”, so these are paid search engine results, rather than the unpaid organic ones above.
This shows the first 10 rows, but it says there are 141 rows. There’s an arrow to go to the next page, but if you click on it, it doesn’t work. That’s because we’re in Edit mode right now. To get this control to work, we need to switch to View mode, so click the View button.
Now you can click on the arrow and get to the next page. You can also do a couple of other things, like reverse the sort order...or export it to a CSV file or to Google Sheets.
Let’s go back to editing. Now click on the table again, so the Properties pane will come up on the right. You can change the chart type by selecting a different one over here. Here’s what it looks like as a pie chart...or a bar chart. Now try the time series chart.
Hmmm. This shows a lot more data. That’s because it added a time dimension since this is a time series chart. It shows the dates along the bottom.
So, when we brought up the table before, it didn’t show us all of the data. It only showed the medium and the total number of sessions. Let’s click on Table again and see what else is available. Click “Add a dimension”. Wow, that’s a lot of dimensions. It’s actually even more than it looks like, because if you click on one of them, it’ll bring up lots of subdimensions.
Let’s add the web pages that people visited. It’s under Page Tracking. Select “Page Title”. The column isn’t very wide, so stretch it out.
Now change it to a pie chart again to see what happens. It doesn’t look any different. That’s because you can only have one dimension in a pie chart. And it’s still using Medium because that’s the first dimension we chose. You can change it by clicking here. There’s an easier way, though, if you don’t remember where you found the Page Title dimension. Go back to the Table and remove the Medium dimension. Now when you go back to the pie chart, it shows the Page Title dimension.
If you make it a bar chart, you’ll see that you can add multiple dimensions, so it all depends on what type of chart you’re using.
You probably noticed that there’s also a Metric chooser below the Dimension chooser. It contains the Sessions metric. If you click “Add a metric”, you’ll see lots of choices again. In fact, they look like the same choices as before. However, if you click on “Page Tracking”, you’ll see different options than last time.
A metric is a number associated with the dimension. In a bar chart, the dimension is the set of categories along the bottom of the graph, such as page titles, in this case. The metric is what determines the height of the bar, such as the number of sessions, in this case.
If we add another metric, such as “Bounces”, then it will add another set of bars to show how many bounces there were per page. A bounce is when a person enters the website through that page and then leaves without going anywhere else.
OK, let’s add a couple of controls to this graph to make it more useful. First, we can add a date range picker. It’s right here. Now draw a box somewhere near the chart. I’ll put mine up here. Then switch back to view mode so you can see how it works. Pick a start date and an end date. The scale changed on the left side of the graph because I shortened the time period.
Another useful widget is the filter control. Go back to edit mode and click here. Then draw another box. Now go back into view mode again. I’ll just make that a little bigger first.
It lets you select which traffic sources to include in the chart. You can either uncheck the ones you don’t want or you can click here to uncheck them all and then check the ones you want. If you only check “referral”, for example, the bounce rate for the home page changes pretty dramatically.
Alright, before we go, let’s add a title. Go back to edit mode and select this text icon. Then, as usual, draw a box. I’m going to call mine, “Sessions vs. Bounces by Page”. You can change the font size and the alignment in the Properties pane.
OK, now you’ve created a nice little report and it was pretty easy, right? In the next lesson, I’ll show you how to get data from BigQuery into Data Studio.
Guy launched his first training website in 1995 and he's been helping people learn IT technologies ever since. He has been a sysadmin, instructor, sales engineer, IT manager, and entrepreneur. In his most recent venture, he founded and led a cloud-based training infrastructure company that provided virtual labs for some of the largest software vendors in the world. Guy’s passion is making complex technology easy to understand. His activities outside of work have included riding an elephant and skydiving (although not at the same time).